Careful with That! Observation of Human Movements to Estimate Objects Properties

TL;DR

This study explores how robots can interpret human movements to assess object care and weight, demonstrating success in identifying careful handling but not in distinguishing object weight using visual data and machine learning.

Contribution

The paper introduces a method for robots to infer care levels in human object manipulation through visual observation, advancing robotic perception capabilities.

Findings

Robots can reliably detect when humans are careful during object handling.

The approach is ineffective for distinguishing between light and heavy objects.

Machine learning algorithms can interpret visual cues for care assessment.

Abstract

Humans are very effective at interpreting subtle properties of the partner's movement and use this skill to promote smooth interactions. Therefore, robotic platforms that support human partners in daily activities should acquire similar abilities. In this work we focused on the features of human motor actions that communicate insights on the weight of an object and the carefulness required in its manipulation. Our final goal is to enable a robot to autonomously infer the degree of care required in object handling and to discriminate whether the item is light or heavy, just by observing a human manipulation. This preliminary study represents a promising step towards the implementation of those abilities on a robot observing the scene with its camera. Indeed, we succeeded in demonstrating that it is possible to reliably deduct if the human operator is careful when handling an object, through machine learning algorithms relying on the stream of visual acquisition from either a robot camera or from a motion capture system. On the other hand, we observed that the same approach is inadequate to discriminate between light and heavy objects.